寄生虫对全球健康构成重大威胁,主要是在缺乏先进医疗设施的地区。早期和准确地检测寄生生物对于挽救生命至关重要。深度学习模型通过在诊断方面提供有希望的结果,提升了医疗部门。检测,并对疾病进行分类。本文探讨了深度学习技术在检测和分类各种寄生生物中的作用。这项研究是在一个由34,298个寄生虫样本组成的数据集上进行的,例如弓形虫,锥体,疟原虫,利什曼原虫,巴贝西亚,和毛滴虫以及宿主细胞,如红细胞和白细胞。这些图像最初从RGB转换为灰度,然后计算形态特征,如周长,高度,area,和宽度。稍后,应用Otsu阈值和分水岭技术来区分前景与背景,并在图像上创建标记以识别感兴趣的区域。深度迁移学习模型,如VGG19、InceptionV3、ResNet50V2、ResNet152V2、EfficientNetB3、EfficientNetB0、MobileNetV2、Xception、DenseNet169和混合模型,使用InceptionResNetV2。这些模型的参数使用三个优化器进行微调:SGD,RMSProp,还有亚当.实验结果表明,当应用RMSProp时,VGG19、InceptionV3和EfficientNetB0实现了99.1%的最高精度,损失为0.09。同样,使用SGD优化器,InceptionV3表现异常出色,实现99.91%的最高精度,损失0.98。最后,应用Adam优化器,InceptionResNetV2表现出色,实现99.96%的最高精度,损失0.13,优于其他优化器。这项研究的结果表明,使用深度学习模型与图像处理方法相结合可以产生一种高度准确和有效的方法来检测和分类寄生生物。
Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and
Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the
Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.